BioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis

Leandro A. Passos, Douglas Rodrigues, Danilo Jodas, Kelton A.P. Costa, Ahsan Adeel, João Paulo Papa
São Paulo State University - UNESP
University of Stirling
Poster BioNeRF

View samples from LLFF dataset generated through BioNeRF.


We present BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields. Since NeRF relies on the network weights to store the scene's 3-dimensional representation, BioNeRF implements a cognitive-inspired mechanism that fuses inputs from multiple sources into a memory-like structure, improving the storing capacity and extracting more intrinsic and correlated information. BioNeRF also mimics a behavior observed in pyramidal cells concerning contextual information, in which the memory is provided as the context and combined with the inputs of two subsequent neural models, one responsible for producing the volumetric densities and the other the colors used to render the scene. Experimental results show that BioNeRF outperforms state-of-the-art results concerning a quality measure that encodes human perception in two datasets: real-world images and synthetic data.



Here is an overview pipeline for BioNeRF, we will walk through each component in this guide.

Positional Feature Extraction

The first step consists of feeding two neural models simultaneously, namely MΔ and Mc, with the camera positional information. The output of these models encodes the positional information from the input image. Although the input is the same, the neural models do not share weights and follow a different flow in the next steps.

Cognitive Filtering

This step performs a series of operations, called filters, that work on the embeddings coming from the previous step. There are four filters this step derives: density, color, memory, and modulation.

Memory Updating

Updating the memory requires the implementation of a mechanism capable of obliterating trivial information, which is performed using the memory filter (Step 3.1 in the figure). Fist, one needs to compute a signal modulation μ, for further introducing new experiences in the memory Ψ through the modulating variable μ using a tanh function (Step 3.2 in the figure).

Contextual Inference

This step is responsible for adding contextual information to BioNeRF. Two new embeddings are generated, i.e., hΔ and hc based on density and color filters, respectively (Step 4 in the figure), which further feed two neural models, i.e., MΔ and Mc. Subsequently, MΔ outputs the volume density, while color information is predicted by Mc, further used to compute the final predicted pixel information and the loss function.

Blender video samples generated by BioNeRF

LLFF video samples generated by BioNeRF


Blender (synthetic)

drums materials ficus ship mic chair lego hotdog AVG
PSNR 25.66 29.74 29.56 29.57 33.38 34.63 31.82 37.23 31.45
SSIM 0.927 0.957 0.965 0.874 0.978 0.977 0.963 0.980 0.953
LPIPS 0.047 0.018 0.017 0.068 0.018 0.011 0.016 0.010 0.026
Blender images

Ground-truth (top) and synthetic view (bottom) images generated by BioNeRF regarding four Realistic Blender dataset’s scenes.

LLFF (real)

Fern Flower Fortress Horns Leaves Orchids Room T-Rex AVG
PSNR 25.17 27.89 32.34 27.99 22.23 20.80 30.75 27.56 27.01
SSIM 0.837 0.873 0.914 0.882 0.796 0.714 0.911 0.911 0.861
LPIPS 0.093 0.055 0.025 0.070 0.103 0.122 0.029 0.044 0.068
LLFF images

Ground-truth (top) and synthetic view (bottom) images generated by BioNeRF regarding four LLFF dataset’s scenes.


        title={BioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis},
        author={Passos, Leandro A and Rodrigues, Douglas and Jodas, Danilo and Costa, Kelton AP, Adeel, Ahsan and Papa, Jo{\~a}o Paulo},
        journal={arXiv preprint arXiv:2402.07310},